香烟盒透明纸包装异常(透明纸没有或不完全)的图像检测

通过对香烟透明纸包装过程中出现无透明纸包装或不完全包装异常现象的图像特征的分析．设计了检测算法．并开发了相关软件系统．经过实例检验，系统可以有效的对烟包透明纸包装过程中出现的上述现象进行检测．而且它也能满足香烟生产企业在线检测的需要。本方法具有自动化程度高，判决速度快，算法简单等优点。     

C600转烟器烟包缺陷在线图像检测装置的设计

为解决某烟厂C600包装机存在缺陷烟包漏检导致流入市场等问题,利用图像在线分析技术,设计了一套烟包在线图像检测装置.该装置主要由一体化工业摄像机和光源、图像采集控制器、上位机、人机交互界面、多维调节支架等部件组成,可以对烟包容易产生缺陷的前端面、侧端面进行在线图像识别分析,避免缺陷烟包进入下道关键工序.实验结果表明:C600转烟器烟包缺陷在线图像检测装置填补了包装机转烟器旋转烟包监测技术空白,监测速度快、精度高,满足香烟包装质量检测的需求.     

空心圆柱端面缺陷的快速视觉检测

提出了一种用于空心圆柱端面缺陷快速检测的新方法.为适应不同尺寸工件以及工件不同端面的几何特征,采用入射角可变的矩形LED阵列作为光源,通过CCD进行图像采集并进行适当的预处理,约化图像中的有用信息以提高检测效率.工件端面检测采用优化的Hough变换法实现标准圆拟合,通过计算实际像素点至拟合圆的径向偏移量以获得缺陷像素点,实现端面缺陷定量评价.实验结果表明,该方法具有较高的检测速度与精度,可用于空心圆柱等小尺寸工件端面质量的在线检验,提高现有的生产线效率.     

基于Hough变换与最小二乘拟合的香烟小包拉线错牙检测方法

针对小盒香烟包装生产过程中经常出现外包装透明薄膜上没有拉线或拉线错牙等现象,提出一种基于图像处理技术的检测方法。首先,对烟包拉线图像进行边缘检测,获得拉线的轮廓;其次,利用Hough变换检测小盒拉线的上下边缘;当Hough变换无法完全找到拉线的上下边界时,采用最小二乘算法对左右两侧拉线弧进行圆拟合,最后根据计算出的拉线宽度或两圆心的垂直距离判断拉线是否错牙并计算出拉线错牙偏移量。实验结果表明所提方法能有效地检测出烟包拉线的错牙程度,且具有较好的鲁棒性和有效性。     

基于Hough变换的平行直线检测改进算法研究

根据经典Hough变换检测空间直线的原理,提出了一种基于Hough变换的平行直线检测改进算法,将采集到的图像进行二值化处理,再使用Sobel算子对该二值化图像进行边缘检测,对边缘检测得到的图像进行Hough变换和峰值点检测,由峰值点得到直线段图像,最后使用本文提出的改进算法计算出所有直线段的斜率,利用斜率相等来判定平行直线.通过对比实验证明,该算法能得到更高的精度,并缩短了运行时间.     

基于Hough变换的建筑边缘特征检测的研究及其改进

Hough变换是对二值图像进行直线检测的有效方法,介绍了Hough变换直线检测对房屋边缘特征检测方法及实验结果,并根据其存在的缺点提出了检测矩形的Hough变换的改进算法.     

基于Hough变换的植物叶脉检测新方法

针对植物叶脉的特点,提出了利用灰度拉伸、Hough变换与边缘生长、图像腐蚀与膨胀进行植物叶脉检测的新方法。在该方法中,Hough变换检测植物叶脉图像的同时也较好的消除了图像噪声,该方法应用到植物叶脉检测中效果较好。     

基于改进Hough变换的车道线检测技术

为提高车道线识别的实时性和可靠性,提出了一种基于改进Hough变换的车道线检测方法;在图像预处理时对不同光照图像进行分类处理,得到二值化图像;利用极角约束Hough变换进行车道线初始定位;根据前一帧图像信息使用基于动态ROI的Hough变换进行车道跟踪;算法加入了车道线检测失效判别模块,以提高检测的可靠性;由于该方法减少了图像空间中被投票的目标点数,缩小Hough变换的投票空间,在一定程度上提高了车道检测的实时性和稳定性;实验结果表明,在结构化道路上,对于不同的路况,算法均具有较好的实时性和鲁棒性。     

一种改进的Hough变换直线检测算法

为了能有效解决Hough变换的计算量大的问题,文中提出了一种基于直线局部结构特征的Hough变换改进的直线检测算法.该算法根据Freeman准则分析了直线上基元的特征信息,通过图像上邻近的同类基元的倾斜角约束基元上像素点的极角范围,减少每个点的计算次数,在保持精度的同时,提高直线检测的速度,在有噪声的情况下,该算法相对标准Hough变换算法可以提高到6到7倍.     

基于Hough变换的PDF417条码图像倾斜矫正

在对PDF417条码图像扫描输入的过程中,条码图像不可避免地发生倾斜,因此必需先对图像进行倾斜矫正,才能实现条码的准确识别.Hough变换具有抗噪声能力强的特点,是一种检测图像倾斜角度的重要方法.为了克服Hough变换计算量大的缺点,先通过行差运算提取图像水平边缘,然后只对提取的边缘进行两级Hough变换,实现倾斜角度的检测.本文采用基于直线拟合的方法实现倾斜图像的矫正.与传统的Hough变换以及其它的倾斜图像矫正方法相比,本文实现了准确性与快速性的很好结合.     

Decentralized damage detection of seismically-excited buildings using multiple banks of Kalman estimators

Natural hazards result in ill-conditioned structures with unfavorable damage. To early recognize damage existence, structures can be screened by damage detection methods after a critical hazard event. These damage detection methods are often developed based on a centralized acquiring and computing system that challenges the feasibility of deployment in a large-scale structure. Decentralized damage detection methods alter a single system to multiple subsystems that allow spatially distributing in a structure and yield comparable performance with the centralized approach. In this study, a decentralized damage detection method based on modal prediction errors via multiple banks of Kalman estimators is proposed. First, a sensor network is comprised of multiple subsystems over a structure of which the subsystems have overlapped sensing nodes. These subsystems are individually identified by an input–output frequency-domain system identification method under ambient vibrations. The identified models are then converted into several banks of Kalman estimators, and the estimators generate the estimation of structural modal responses. The prediction errors are calculated from the differentiation between measured and estimated modal responses, and the accumulated standard deviations of modal prediction errors serve as the damage indices for recognizing the damage occurrence, locations, and levels. A numerical example is introduced to demonstrate the proposed method as well as to evaluate the detection effectiveness. Moreover, the proposed method is also experimentally verified by a scaled twin-tower building using shake table testing. The experimental results indicate that the proposed method is quite effective to inform damage of structures in terms of damage occurrence, locations, and levels.     

基于PVDF传感器和工作曲率变形实现裂纹梁裂纹检测

提出通过压电高分子薄膜PVDF(Polyvinylidene Fluoride)作为传感器直接测量结构的工作曲率变形(Curvature Operating Shape)实现结构裂纹检测.以悬臂裂纹梁为例,首先探讨基于PVDF传感器测量工作曲率变形,并用于结构损伤检测的可行性,然后介绍工作曲率变形用于损伤检测的理论基础,并以PVDF压电薄膜作为传感器,提出只通过损伤后结构的频率响应函数,直接得到工作曲率变形和损伤指标.最后通过对一条裂纹和两条裂纹悬臂梁的损伤检测实验,证明该方法在梁结构损伤识别和健康监测中的有效性,并且识别过程不需要健康结构的振动信息.     

悬臂梁结构损伤检测仿真研究

以悬臂梁结构为研究对象,针对户外条件下结构损伤检测过程复杂、周期长、检测效果不理想的问题,提出了模态曲率变化率法.通过大型商用有限元软件ABAQUS,建立梁结构有限元模型,基于模态曲率变化率法构建损伤指标,对梁结构进行损伤检测定位,并与模态应变能法的损伤检测效果进行了对比.结果表明:模态曲率变化率法计算过程简单,能够准确快捷地对悬臂梁结构进行损伤识别定位.     

Structural damage detection using bi-temporal optical satellite images

Multi-temporal satellite imagery is now available at sub-metre accuracy and has been found to be very useful for performing rapid damage assessment on human settlement areas affected by large-scale disasters. In this article, a method of formulating structural damage detection measures based on pre- and post-disaster satellite images is proposed. To validate the proposed damage measures, building-based structural damage assessment is conducted. First, their effectiveness in representing multilevel structural damage is demonstrated using synthetic patterns of building damage. Second, the damage classification accuracy is evaluated by means of a pattern classification approach applied to a pair of bi-temporal satellite images, wherein earthquake damage to hundreds of buildings is assessed. The article concludes that the proposed damage detection measures, which are conceptually simple and computationally efficient, outperform traditional measures, such as linear correlation coefficients.     

航空发动机损伤图像的二分类到多分类递进式检测网络

航空发动机损伤是影响飞行安全的重要因素。当前基于计算机视觉的发动机孔探图像损伤检测存在两个主要问题：一是孔探图像背景复杂,使得模型对损伤的检测精度较低;二是孔探图像数据来源受限,导致模型可检测类别较少。为解决这两个问题,提出了基于Mask R-CNN的二分类到多分类递进式航空发动机损伤图像检测网络。通过在Mask R-CNN中增加二分类检测分支,首先对图像中的损伤进行二分类检测并对定位坐标进行回归优化;其次使用原始检测分支递进地进行多分类检测,以进一步回归优化损伤的检测结果并确定损伤类型;最后根据多分类检测的结果,通过Mask分支对对损伤进行实例分割。为了增加模型检测类别及验证方法的有效性,构建了包含八种损伤类型,共1 315张孔探图像的数据集。在该集合上进行的训练和测试结果表明,多分类检测的平均精度（AP）和AP75与Mask R-CNN相比分别提高3.34%、9.71%,可见所提方法能够有效提高对孔探图像中的损伤的多分类检测精度。     

基于轻量级双层金字塔结构的伞裙破损检测算法

在无人机电力线路巡检系统中，针对伞裙破损检测的准确性和实时性，该文提出了一种基于双层金字塔结构的轻量级伞裙破损检测算法，通过充分提取图片中高低级语义特征，可有效防止有用信息被过滤。同时，利用多通道特征增加了模型特征表达能力。此外，该文还利用深度可分离卷积来优化卷积内核性能，使其可以在低功耗嵌入式系统上实时运行。实验结果表明，该算法在不同光照、拍摄角度和破损模糊条件下，对伞裙破损检测均有较好的准确性、实时性和鲁棒性。     

薄壁异型焊接构件疲劳损伤检测方法研究

针对传统的焊缝疲劳损伤检测方法灵敏度较低的问题,提出薄壁异型焊接构件焊缝疲劳损伤检测方法。根据不同状态下的焊缝对振动的动态响应,识别构件焊缝是否存在疲劳损伤;使用超声波完成对构件焊缝疲劳损伤的检测,提取探头接收信号的基波和谐波,对信号进行傅里叶变换和Hilbert-Huang变换,提取基波和谐波中焊缝疲劳损伤信号特征;在支持向量机中,使用特征样本集完成对焊缝疲劳损伤检测。方法有效性验证的试验表明,该方法灵敏度更高,性能更优越。     

Structural damage detection using imperialist competitive algorithm and damage function

In practical damage detection problems, experimental modal data is only available for a limited number of modes and in each mode, only a limited number of nodal points are recorded. In using modal data, the majority of the available damage detection solution techniques either require data for all the modes, or all the nodal data for a number of modes; neither of which may be practically available through experiments. In the present study, damage identification is carried out using only a limited number of nodal data of a limited number of modes. The proposed method uses the imperialist competitive optimization algorithm and damage functions. To decrease the number of design variables, several bilinear damage functions are defined to model the damage distribution. Damage functions with both variable widths and variable weights are proposed for increased accurately. Four different types of objective functions which use modal responses of damaged structure are investigated with the aim of finding the most suitable function. The efficiency of the proposed method is investigated using three benchmark numerical examples using both clean and noisy modal data. It is shown that by only using a limited number of modal data, the proposed method is capable of accurately detecting damage locations and reasonably accurately evaluate their extents. The proposed algorithm is most effective with noisy modal data, compared to other available solutions.     

Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

It is proposed in this paper a novel two-stage structural damage detection approach using fuzzy neural networks (FNNs) and data fusion techniques. The method is used for structural health monitoring and damage detection, particularly for cases where the measurement data is enormous and with uncertainties. In the first stage of structural damage detection, structural modal parameters derived from structural vibration responses are fed into an FNN as the input. The output values from the FNN are defuzzified to produce a rough structural damage assessment. Later, in the second stage, the values output from three different FNN models are input directly to the data fusion center where fusion computation is performed. The final fusion decision is made by filtering the result with a threshold function, hence a refined structural damage assessment of superior reliability. The proposed approach has been applied to a 7-degree of freedom building model for structural damage detection, and proves to be feasible, efficient and satisfactory. Furthermore, the simulation result also shows that the identification accuracy can be boosted with the proposed approach instead of FNN models alone.     

A three-stage damage detection method for large-scale space structures using forward substructuring approach and enhanced bat optimization algorithm

In this study, an efficient three-stage method is proposed for damage detection of large-scale space structures by employing forward substructuring approach, modal strain energy and enhanced bat algorithm (EBA) optimization. EBA is a modified version of BA that is proposed in this paper and used a passive congregation operator to improve the performance of standard BA. In the first stage, the global structure is divided into manageable substructures. The stiffness matrices of independent substructures are obtained based on Kron’s substructuring method. Then a modal strain energy-based index is employed to precisely locate the eventual damages of the structure. In the third stage, damage severities are estimated via EBA using the second-stage results. To demonstrate the ability of the proposed method for detection of multiple structural damages, large-scale space structures with different types of damage scenarios are considered. The results show that the proposed method can detect the exact locations and severity of damages highly accurate in space structures.